class: right, middle, inverse, titular # Mobile genetic elements (MGEs) in the gut microbiota. ### **Mirna Vázquez Rosas Landa** ### 2022/04/04 --- # Mobile genetic elements (MGEs) in gut metagenomes In the beginning the mobile genetic elements were associated with pathogenic strains; however, this has changed with time [Dobrindt et al.2004](https://pubmed.ncbi.nlm.nih.gov/15100694/). For example, an immense diversity of phages has been described in the ocean, suggesting that these mobile elements might play a relevant role in microbial communities working as seed banks for adaptive genes which any community member can potentially acquire ([Fancello et al.2011](https://pubmed.ncbi.nlm.nih.gov/21816767/); [Modi et al.2013](https://pubmed.ncbi.nlm.nih.gov/23748443/); [Subirats et al.2016](https://pubmed.ncbi.nlm.nih.gov/27312355/)). I started my search by looking for these three types of mobile elements. - Insertion sequences - Integrons - Phages --- # Insertion sequence elements in the gut metagenome - To search for insertion elements I used [ISEScan](https://github.com/xiezhq/ISEScan). This program is a python pipeline that identifies elements requiring a nucleotide sequence as the input and identifies both **complete** and **partial** Insertion Sequence (IS). --- # Install **ISEScan** - Create a conda environment ```bash conda create -n isescan_env ``` - Activate the conda environment ```bash conda activate isescan_env ``` - Add Bioconda channels ```bash conda config --add channels defaults conda config --add channels bioconda conda config --add channels conda-forge ``` - Conda install ```bash conda install -n isescan_env -c conda isescan ``` --- # Run **ISEScan** ```bash nohup isescan.py --seqfile 0118A1023.contigs.fa --output isescan_gut_microbiota --nthread 20 & ``` --- # Explore the output of **ISEScan** - The program uses [FragGeneScan](https://pubmed.ncbi.nlm.nih.gov/20805240/) to make the protein prediction, so one first exciting thing to look at is **how many proteins were inferred?** ```bash grep -c ">" 0118A1023.contigs.fa.faa ``` Number of sequences: 15,164 - **How many elements were inferred?** One easy way to do it is to count the number of rows in the output file. ```bash wc -l 0118A1023.contigs.fa.tsv ``` Number of elements: 71 --- # Explore the output of **ISEScan** - We can take a quick look to the output file. <div id="htmlwidget-f0d8c5cd11e38aead128" style="width:90%;height:auto;" class="datatables html-widget"></div> <script type="application/json" 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class=\"compact\">\n <thead>\n <tr>\n <th>seqID<\/th>\n <th>family<\/th>\n <th>cluster<\/th>\n <th>isBegin<\/th>\n <th>isEnd<\/th>\n <th>isLen<\/th>\n <th>ncopy4is<\/th>\n <th>start1<\/th>\n <th>end1<\/th>\n <th>start2<\/th>\n <th>end2<\/th>\n <th>score<\/th>\n <th>irId<\/th>\n <th>irLen<\/th>\n <th>nGaps<\/th>\n <th>orfBegin<\/th>\n <th>orfEnd<\/th>\n <th>strand<\/th>\n <th>orfLen<\/th>\n <th>E-value<\/th>\n <th>E-value4copy<\/th>\n <th>type<\/th>\n <th>ov<\/th>\n <th>tir<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":8,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,22]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[8,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- # Explore the output of **ISEScan** .right-column[ - Which families are more abundant? ```r more_abundant<-Insertion %>% count(family) %>% # Count the number of elements in the family column arrange(-n) # Arrange the number from the from largest to smallest head(more_abundant) # Show the top six ## # A tibble: 6 × 2 ## family n ## <chr> <int> ## 1 IS21 15 ## 2 IS3 9 ## 3 IS256 6 ## 4 IS66 6 ## 5 IS982 6 ## 6 IS1182 5 ``` ] .left-column[ The most abundant element is: - [IS21](https://tnpedia.fcav.unesp.br/index.php/IS_Families/IS21_family) ] --- # Complete and partial elements Some of the IS elements can be **incomplete**, which may reflect ancestral transposition events ([Siguier et al., 2014](https://academic.oup.com/femsre/article/38/5/865/496682)) suggesting that the rest of the key sequence features were lost over time. Therefore, I focused at first on the complete elements, considering they are more likely to be **recent** and **active** in the community. --- # Explore the output **ISEScan** **ISEScan** has an extra column indicating if the element is **complete** or **partial**; therefore, I filtered the output file to find the most abundant complete elements in the metagenome. .right-column[ - Which ISs are complete? ```r Complete<-Insertion %>% filter(type == "c") %>% # Filter the rows that had a "c" in the type column, that stands for complete count(family) %>% arrange(-n) head(Complete) ## # A tibble: 6 × 2 ## family n ## <chr> <int> ## 1 IS1182 4 ## 2 IS3 4 ## 3 IS30 3 ## 4 IS66 3 ## 5 IS256 2 ## 6 IS4 2 ``` ] .left-column[ The most abundant elements that are also **complete** and probably more recent are: - [IS1182](https://tnpedia.fcav.unesp.br/index.php/IS_Families/IS5_and_related_IS1182_families) - [IS3](https://tnpedia.fcav.unesp.br/index.php/IS_Families/IS3_family) ] --- # **New hypothesis!!!** - What if there are **partial** sequences that also contain transposases? - Maybe the rest of the sequence is highly divergent, and that is why we can not find any matches to the databases; therefore, the sequences are classified as partial? - If they have transposases, that might mean that they must be elements that could be actively moving something in the community, but since they are divergent and new, the program is not able to classify them (properly). --- # Explore the output of **ISEScan** to look for potential new elements. - I ran InterproScan to identify transposases domains in the sequences that were predicted with [FragGeneScan](https://pubmed.ncbi.nlm.nih.gov/20805240/) by **ISEScan**. ```bash interproscan.sh -cpu 20 -goterms -pa -i 0118A1023.contigs.fa.orf_interpr.faa > Log_Interpro_Scan_$i.txt ``` - Then I extracted a list of the IDs of the **partial** sequences. ```r list_partial<-Insertion %>% filter(type == "p") %>% # Here I am filtering the output table to extract the "partial" hits. select(seqID, orfBegin, orfEnd, strand) %>% # Select certain columns on the table unite("seq_name", c("seqID", "orfBegin", "orfEnd", "strand"), sep="_") %>% # The output of ISEScan uses as sequence ID the name of the scaffold so I create a new colum merging the information from the start and end of the sequence to be able to match the results with InterproScan distinct() %>% # Remove duplicated pull() # Create a vector ``` --- # Explore the output of **ISEScan** to look for potential new elements. - I read the output and filtered the sequences based on the list. ```r interpro<-read_delim("../data/0118A1023.contigs.fa.orf_interpro.faa.tsv", delim="\t", col_names = F) %>% filter(X1 %in% list_partial) %>% # Filter the table using the IDs extracted previously filter(X4 == "Pfam") %>% # Filter the output to get the PFAM domains select(X1, X5, X6) %>% filter(str_detect(X6, "Transposase")) # Search for elements annotated as transposases ``` <div id="htmlwidget-aa36206aed781b512de2" style="width:95%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-aa36206aed781b512de2">{"x":{"filter":"none","vertical":false,"editable":{"target":"cell","disable":{"columns":null},"numeric":[],"area":[]},"data":[["qb307082014_I_scaffold_1602_331_1806_+","qb307082014_I_scaffold_737_6236_6776_+","qb307082014_I_scaffold_1076_937_1413_+","qb307082014_I_scaffold_34_1124_1628_+","qb307082014_I_scaffold_110_20659_21634_-","qb307082014_I_scaffold_497_1_926_-","qb307082014_I_scaffold_80_24180_24848_+","qb307082014_I_scaffold_178_3066_3849_-","qb307082014_I_scaffold_178_3066_3849_-","qb307082014_I_scaffold_459_3729_4728_+","qb307082014_I_scaffold_57_45355_46584_+","qb307082014_I_scaffold_50_37681_38583_+","qb307082014_I_scaffold_591_2177_3162_+","qb307082014_I_scaffold_1186_1_1145_-","qb307082014_I_scaffold_7_283037_283571_+","qb307082014_I_scaffold_802_122_1348_+","qb307082014_I_scaffold_250_9309_9812_-"],["PF03050","PF00872","PF13612","PF00872","PF00872","PF00872","PF01609","PF01548","PF02371","PF01797","PF01609","PF01609","PF13612","PF01610","PF13751","PF01609","PF13612"],["Transposase IS66 family","Transposase, Mutator family","Transposase DDE domain","Transposase, Mutator family","Transposase, Mutator family","Transposase, Mutator family","Transposase DDE domain","Transposase","Transposase IS116/IS110/IS902 family","Transposase IS200 like","Transposase DDE domain","Transposase DDE domain","Transposase DDE domain","Transposase","Transposase DDE domain","Transposase DDE domain","Transposase DDE domain"]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>X1<\/th>\n <th>X5<\/th>\n <th>X6<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":6,"scrollX":true,"columnDefs":[],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[6,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- # Conclusion of **ISEScan** - The most abundant element in the dataset is **partial** IS21. - The elements IS1182 and IS3 are the most abundant **complete** elements. - The analysis showed that there are **17 potential new mobile elements** in the dataset. --- # Integron prediction in the gut metagenome. - Integrons are assembly platforms — DNA elements that acquire open reading frames embedded in exogenous gene cassettes and convert them to functional genes by ensuring their correct expression ([Mazel., 2006](https://www.nature.com/articles/nrmicro1462)). - I used [Integron Finder](https://github.com/gem-pasteur/Integron_Finder) to identify the integrons in the metagenome. --- # Install **Integron Finder** - Create a conda environment ```bash conda create -n integronFinder ``` - Activate the conda environment ```bash conda activate integronFinder ``` - Conda install ```bash conda install integron_finder -n integronFinder ``` --- # Run **Integron Finder** ```bash integron_finder --local-max --func-annot 0118A1023.contigs.fa ``` --- # Explore **Integron Finder** output The output of the program contain 3 files: - **0118A1023.contigs.integrons**: A file with all integrons and their elements detected in all sequences in the input file. - **0118A1023.contigs.summary**: A summary file with the number and type of integrons per sequence. - **integron_finder.out**: A copy standard output. --- # Explore **Integron Finder** output Lets see the **0118A1023.contigs.integrons** file: ```r integrons<-read_delim("../data/0118A1023.contigs.integrons", delim = "\t", skip = 1) ``` <div id="htmlwidget-60d24bf6df7856dd6a47" style="width:95%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-60d24bf6df7856dd6a47">{"x":{"filter":"none","vertical":false,"editable":{"target":"cell","disable":{"columns":null},"numeric":[3,4,5,6,12],"area":[]},"data":[["integron_01","integron_01"],["qb307082014_I_scaffold_18","qb307082014_I_scaffold_18"],["attc_001","attc_002"],[120781,122724],[120823,122771],[-1,-1],[0.43,0.43],["attC","attC"],["attC","attC"],["attc_4","attc_4"],["CALIN","CALIN"],["No","No"],[null,1901],["lin","lin"]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>ID_integron<\/th>\n <th>ID_replicon<\/th>\n <th>element<\/th>\n <th>pos_beg<\/th>\n <th>pos_end<\/th>\n <th>strand<\/th>\n <th>evalue<\/th>\n <th>type_elt<\/th>\n <th>annotation<\/th>\n <th>model<\/th>\n <th>type<\/th>\n <th>default<\/th>\n <th>distance_2attC<\/th>\n <th>considered_topology<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":3,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[3,4,5,6,12]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[3,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> Integron finder can predict three types of elements described in [Cury et al., 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889954/): - **Complete integrons**: Include an integrase and at least one attC site - **CALIN**: The clusters of attC sites lacking integron-integrases (CALIN) are composed of at least two attC sites. - **In0**: The In0 elements are composed of an integron integrase and no attC sites. In this metagenome, I was only able to identify two **CALIN** elements. --- # **New hypothesis!!!** - **What genes are present in qb307082014_I_scaffold_18?** - Exploring the neighborhood genes to the **CALIN** element would tell us maybe more about **what are these sequences doing** and **why are they potentially important for the environment?** - I used [OperonMapper](https://biocomputo.ibt.unam.mx/operon_mapper/) to predict the genes that are near the **CALIN** element predicted. --- # Operon Mapper output The output files from Operon Mapper are: - predicted_protein_sequences_417647 - predicted_orfs_417647 - predicted_COGs_417647 - operonic_gene_pairs_417647 - **list_of_operons_417647** - functional_descriptions_417647 - ORFs_coordinates_417647 The **list_of_operons_417647** file contains the annotation of the elements and the number of operons predicted. --- # Operon Mapper output Exploring the **list_of_operons_417647** file. ```r Operon_mapper<-read_delim("../data/417647/list_of_operons_417647", delim="\t") %>% fill(Operon) # Fill NA values with the previous value in a column ``` <div id="htmlwidget-c24c3206d90f000ccaf3" style="width:95%;height:auto;" class="datatables html-widget"></div> <script type="application/json" 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Uncharacterized conserved protein ",null,"[R] Predicted lactoylglutathione lyase ",null,"[S] Uncharacterized conserved protein ",null,"[U] Signal recognition particle GTPase ","[R] Predicted hydrolases of the HAD superfamily","[D] Chromosome segregation ATPases ","[K] dsRNA-specific ribonuclease ",null,"[E] ABC-type dipeptide transport system, periplasmic component","[EP] ABC-type dipeptide/oligopeptide/nickel transport systems, permease components","[EP] ABC-type dipeptide/oligopeptide/nickel transport systems, permease components","[E] ABC-type oligopeptide transport system, ATPase component","[EP] ABC-type dipeptide/oligopeptide/nickel transport system, ATPase component",null,"[IQ] Acyl carrier protein ","[I] Fatty acid/phospholipid biosynthesis enzyme ","[LK] RecG-like helicase ","[R] Predicted kinase related to dihydroxyacetone kinase","[S] Uncharacterized protein conserved in bacteria ",null,"[KT] Response regulator of the LytR/AlgR family","[T] Signal transduction histidine kinase regulating citrate/malate",null,"[L] Transposase and inactivated derivatives, IS30 family",null,null,null,"[CP] ABC-type Na+ efflux pump, permease component","[R] ABC-type uncharacterized transport system, ATPase component",null,"[J] Ribosomal protein L28 ",null,"[H] Thiamine pyrophosphokinase ","[G] Pentose-5-phosphate-3-epimerase ","[R] Predicted GTPases ","[S] Uncharacterized protein conserved in bacteria ","[T] Serine/threonine protein phosphatase ","[J] tRNA and rRNA cytosine-C5-methylases ","[J] Methionyl-tRNA formyltransferase ","[J] N-formylmethionyl-tRNA deformylase ","[L] Primosomal protein N' (replication factor Y)",null,"[K] DNA-directed RNA polymerase, subunit K/omega ","[F] Guanylate kinase ",null,"[M] D-alanyl-D-alanine carboxypeptidase ",null,null,"[S] Uncharacterized stress-induced protein ","[S] Uncharacterized protein conserved in bacteria ",null,"[S] Uncharacterized protein conserved in bacteria ",null,"[G] 2-keto-3-deoxy-6-phosphogluconate aldolase ","[G] D-mannonate dehydratase ","[G] Phosphotransferase system, mannose/fructose-specific component IIA ","[G] Phosphotransferase system, mannose/fructose/N-acetylgalactosamine-specific component IIB ","[G] Phosphotransferase system, mannose/fructose/N-acetylgalactosamine-specific component IID ","[G] Phosphotransferase system, mannose/fructose/N-acetylgalactosamine-specific component IIC ","[C] Glycerol dehydrogenase and related enzymes ","[HE] Phosphoglycerate dehydrogenase and related dehydrogenases ","[G] Predicted 6-phosphogluconate dehydrogenase ",null,"[K] Transcriptional regulators ",null,null,null,"[I] Phosphatidylglycerophosphate synthase ",null,"[T] Signal transduction histidine kinase ",null,"[S] Uncharacterized protein conserved in bacteria ",null,"[R] Predicted Zn-dependent peptidases ","[R] Predicted Zn-dependent peptidases ",null,"[M] Large-conductance mechanosensitive channel ",null,null,null,null,"[K] Transcriptional regulators ",null,"[G] Trehalose and maltose hydrolases (possible phosphorylases)","[R] Predicted phosphatase/phosphohexomutase ",null,null,null,null,null,"[R] Predicted metal-dependent membrane protease ",null,"[FE] Phosphoribosylpyrophosphate synthetase ",null,"[O] Conserved domain frequently associated with peptide",null,"[D] Nucleotide-binding protein implicated in inhibition of","[D] Nucleotide-binding protein implicated in inhibition of","[L] DNA mismatch repair enzyme (predicted ATPase)","[L] Mismatch repair ATPase (MutS family) ","[S] Predicted membrane protein ","[S] Uncharacterized protein conserved in bacteria ","[R] Predicted HD superfamily hydrolase ",null,"[K] Transcriptional antiterminator ","[G] Beta-glucosidase/6-phospho-beta-glucosidase/beta-galactosidase ","[G] Phosphotransferase system cellobiose-specific component IIA ","[G] Phosphotransferase system cellobiose-specific component IIC ","[G] Phosphotransferase system cellobiose-specific component IIB ",null,"[L] RecA/RadA recombinase ","[R] Predicted nucleotide-utilizing enzyme related to molybdopterin-biosynthesis",null,"[F] Adenosine deaminase ",null,"[R] Putative NADH-flavin reductase ","[K] Predicted transcriptional regulator ",null,"[S] Predicted membrane protein ",null,null,null,"[E] Acetylornithine deacetylase/Succinyl-diaminopimelate desuccinylase and related deacylases",null,null,"[K] DNA-directed RNA polymerase specialized sigma subunit,",null,null,"[M] Periplasmic protein TonB, links inner and",null,null,null,null,null,null,"[S] Predicted membrane protein ",null,null,"[I] Esterase/lipase ",null,"[Q] Amidases related to nicotinamidase ",null,"[M] Putative effector of murein hydrolase ","[R] Putative effector of murein hydrolase LrgA","[KT] Response regulator of the LytR/AlgR family","[T] Putative regulator of cell autolysis ",null,"[P] ABC-type metal ion transport system, periplasmic","[P] ABC-type metal ion transport system, permease","[P] ABC-type metal ion transport system, ATPase",null,"[O] Predicted redox protein, regulator of disulfide",null,"[J] Ribosomal protein S14 ","[J] Ribosomal protein L33 ","[R] Putative GTPases (G3E family) ","[K] Predicted transcriptional regulator with an HTH","[P] ABC-type metal ion transport system, periplasmic",null,"[J] tRNA-dihydrouridine synthase ",null,"[V] ABC-type multidrug transport system, permease component","[V] ABC-type multidrug transport system, ATPase component",null,"[KL] DNA or RNA helicases of superfamily","[L] A/G-specific DNA glycosylase ",null,null,null,null,null,null,"[J] Ribosomal protein S9 ","[J] Ribosomal protein L13 ",null,"[P] DNA-binding ferritin-like protein (oxidative damage protectant)",null,null,null,"[G] Sugar (pentulose and hexulose) kinases ",null,"[NOU] Type II secretory pathway, prepilin signal",null,"[K] DNA-directed RNA polymerase, beta' subunit/160 kD","[K] DNA-directed RNA polymerase, beta subunit/140 kD",null,"[R] Predicted nucleic-acid-binding protein containing a Zn-ribbon",null,"[H] Biotin-(acetyl-CoA carboxylase) ligase ",null,null,"[R] Predicted ATPases ",null,"[K] Transcriptional regulator ",null,null,"[U] Signal recognition particle GTPase ",null,null,"[H] 1,4-dihydroxy-2-naphthoate octaprenyltransferase ","[H] Membrane-associated lipoprotein involved in thiamine biosynthesis","[S] Major membrane immunogen, membrane-anchored lipoprotein ",null,"[C] NADH dehydrogenase, FAD-containing subunit ",null,"[S] Uncharacterized protein conserved in bacteria ","[S] Predicted membrane protein ","[H] Geranylgeranyl pyrophosphate synthase ",null,null,null,null,null,null]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Operon<\/th>\n <th>IdGene<\/th>\n <th>Type<\/th>\n <th>COGgene<\/th>\n <th>PosLeft<\/th>\n <th>postRight<\/th>\n <th>Strand<\/th>\n <th>Function<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":6,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[0,4,5]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[6,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- # Operon Mapper output By manually looking at the **list_of_operons_417647** file I found a sequence that is associated to a transposase is [COG2826](https://www.ncbi.nlm.nih.gov/research/cog/cog/COG2826/), and was found in the **qb307082014_I_scaffold_18** scaffold. Suggesting is the one inferred by **Integron Finder**. ```r COG2826<-read_delim("../data/417647/list_of_operons_417647", delim="\t") %>% fill(Operon) %>% drop_na(Type) %>% filter(COGgene == "COG2826") ``` <div id="htmlwidget-39e51313bb84afd044ff" style="width:95%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-39e51313bb84afd044ff">{"x":{"filter":"none","vertical":false,"editable":{"target":"cell","disable":{"columns":null},"numeric":[0,4,5],"area":[]},"data":[[8],["LOOBNNKK_00020"],["CDS"],["COG2826"],[23250],[24209],["-"],["[L] Transposase and inactivated derivatives, IS30 family"]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Operon<\/th>\n <th>IdGene<\/th>\n <th>Type<\/th>\n <th>COGgene<\/th>\n <th>PosLeft<\/th>\n <th>postRight<\/th>\n <th>Strand<\/th>\n <th>Function<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":1,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[0,4,5]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[1,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- # Operon Mapper output I look at the operons that where up and down to see which genes would be there and I found something interesting. ```r operons_to_look<-c("7", "9") neighbour_genes<-read_delim("../data/417647/list_of_operons_417647", delim="\t") %>% fill(Operon) %>% drop_na(Type) %>% filter(Operon %in% operons_to_look) ``` <div id="htmlwidget-1efa9dc22c3733896ba3" style="width:95%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-1efa9dc22c3733896ba3">{"x":{"filter":"none","vertical":false,"editable":{"target":"cell","disable":{"columns":null},"numeric":[0,4,5],"area":[]},"data":[[7,7,9],["LOOBNNKK_00018","LOOBNNKK_00019","LOOBNNKK_00021"],["CDS","CDS","CDS"],["COG3279","COG3290","ROG4413"],[21499,22220,24272],[22218,23050,24613],["-","-","-"],["[KT] Response regulator of the LytR/AlgR family","[T] Signal transduction histidine kinase regulating citrate/malate",null]],"container":"<table class=\"compact\">\n <thead>\n <tr>\n <th>Operon<\/th>\n <th>IdGene<\/th>\n <th>Type<\/th>\n <th>COGgene<\/th>\n <th>PosLeft<\/th>\n <th>postRight<\/th>\n <th>Strand<\/th>\n <th>Function<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"tBp","pageLength":4,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[0,4,5]}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[4,10,25,50,100]}},"evals":[],"jsHooks":[]}</script> --- # Blast search results - The gene in operon 9 has a non identidied function, I extracted the sequence and make a web blast search. - The best hit at the NCBI database was [accessory regulator AgrC](https://www.ncbi.nlm.nih.gov/protein/EGO9012381.1?report=genbank&log$=protalign&blast_rank=2&RID=4R5T83X5013). The same protein at UniProt was [A0A0K2MC93](https://www.uniprot.org/uniprot/A0A0K2MC93), which is classified by Gene Ontology database as [GO:0000155](https://www.ebi.ac.uk/QuickGO/term/GO:0000155): Catalysis of the phosphorylation of a histidine residue in response to detection of an extracellular signal such as a chemical ligand or change in environment, to initiate a change in cell state or activity. The two-component sensor is a histidine kinase that autophosphorylates a histidine residue in its active site. The phosphate is then transferred to an aspartate residue in a downstream response regulator, to trigger a response. --- # Conclusion - In this metagenome I was only able to identify two **CALIN** elements. - These elements were identified in the same scaffold and the Operon Mapper analysis revealed that there is a hypothetical protein cluster nearby involved in signal transduction from the environment. -- I think that the genes within this integron cassette are proteins related to some environmental response to stress, meaning that in this community microbes exchange genes that allows them cope with a changing environment. --- # Phages in the gut metagenome. - I searched for lysogenic phages in the metagenome as these elements are one of the most efficient mechanisms of HGT ([Jiang and Paul 1998](https://pubmed.ncbi.nlm.nih.gov/9687430/); [Weinbauer and Rassouldzadegan 2004](https://pubmed.ncbi.nlm.nih.gov/14686936/); [Moon et al.2016](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151409)). - [PHASTER](https://phaster.ca/). A software that performs a number of database comparisons as well as phage 'cornerstone' feature identification steps to locate, annotate and display prophage sequences and prophage features ([Zhou et al., 2011](https://pubmed.ncbi.nlm.nih.gov/21672955/)). --- # Run PHASTER You can run a bunch of jobs within a loop using the API. I have done it before for one of my previous publications. - [Publication](https://academic.oup.com/femspd/article/75/5/ftx059/3861975?login=true) - [Code](https://github.com/actevol/Comparative_genomics_of_virulence_factors) Here I just run it like that ```bash wget --post-file="0118A1023.contigs.fa" "http://phaster.ca/phaster_api" -O 0118A1023.contigs.fa.phaster ``` And then you have to wait... And I still waiting This way you can access to the job list and see how long is last for your job to run: ```bash wget "http://phaster.ca/phaster_api?acc=ZZ_cd44018c8e" -O Output_filename ``` --- # How would I analyze PHASTER results? After the files were processed, we downloaded the results: ```bash wget -O ID_FROM.phaster "http://phaster.ca/jobs/ID_FROM.phaster/summary.txt" ``` Then I would extract the number or intact prophages in the metagenome. ```bash for i in $(*.fasta.phaster); do grep "intact prophage" $i >>number_phages; done ``` --- # Future directions - Diversity. To determine how divergent are all those elements elements I would do phylogenetic reconstructions of the transposases and the genes that cluster closely to the mobile elements. --- # Future directions .pull-left[ Reconstruct genomes. Reconstructing the genomes would allow me to know in which organism this elements are present and build more accurate hypothesis regarding the ecology and evolution of the mobile elements. Over the MAGs I could run other programs and build a more accurate inferences of the operons: - [OperonMapper](https://biocomputo.ibt.unam.mx/operon_mapper/) to predict the genes that are potentially being transfer within the community. - [IslandViewer](https://www.pathogenomics.sfu.ca/islandviewer/) to identify elements that are actively moving. ] .pull-right[  ] ---